adaptive_avg_pool3d

paddle.nn.functional. adaptive_avg_pool3d ( x, output_size, data_format='NCDHW', name=None ) [source]

This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size.

For avg adaptive pool3d: .. math:

System Message: ERROR/3 (/usr/local/lib/python3.8/site-packages/paddle/nn/functional/pooling.py:docstring of paddle.nn.functional.pooling.adaptive_avg_pool3d, line 6)

Unexpected indentation.

dstart &= floor(i * D_{in} / D_{out})
dend &= ceil((i + 1) * D_{in} / D_{out})
hstart &= floor(j * H_{in} / H_{out})
hend &= ceil((j + 1) * H_{in} / H_{out})
wstart &= floor(k * W_{in} / W_{out})
wend &= ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) &=

System Message: WARNING/2 (/usr/local/lib/python3.8/site-packages/paddle/nn/functional/pooling.py:docstring of paddle.nn.functional.pooling.adaptive_avg_pool3d, line 13)

Block quote ends without a blank line; unexpected unindent.

rac{sum Input[dstart:dend, hstart:hend, wstart:wend]}

{(dend - dstart) * (hend - hstart) * (wend - wstart)}

Args:
x (Tensor): The input tensor of adaptive avg pool3d operator, which is a 5-D tensor.

The data type can be float32, float64.

output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,

it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.

data_format (str): The data format of the input and output data. An optional string

from: “NCDHW”, “NDHWC”. The default is “NCDHW”. When it is “NCDHW”, the data is stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width].

name(str, optional): For detailed information, please refer

to Name. Usually name is no need to set and None by default.

Returns:

Tensor: The output tensor of avg adaptive pool3d result. The data type is same as input tensor.

Examples:
# adaptive avg pool3d
# suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive avg pool performs calculations as follow:
#
#     for i in range(l):
#         for j in range(m):
#             for k in range(n):
#                 dstart = floor(i * D / l)
#                 dend = ceil((i + 1) * D / l)
#                 hstart = floor(j * H / m)
#                 hend = ceil((j + 1) * H / m)
#                 wstart = floor(k * W / n)
#                 wend = ceil((k + 1) * W / n)
#                 output[:, :, i, j, k] =
#                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
import paddle

input_data = paddle.randn(shape=(2, 3, 8, 32, 32))
out = paddle.nn.functional.adaptive_avg_pool3d(
                x = input_data,
                output_size=[3, 3, 3])
# out.shape is [2, 3, 3, 3, 3]